torch.optim.lr_scheduler.StepLR Class Reference

Inheritance diagram for torch.optim.lr_scheduler.StepLR:

Public Member Functions
def	__init__ (self, optimizer, step_size, gamma=0.1, last_epoch=-1)
def	get_lr (self)
Public Member Functions inherited from torch.optim.lr_scheduler._LRScheduler
def	__init__ (self, optimizer, last_epoch=-1)
def	state_dict (self)
def	load_state_dict (self, state_dict)
def	get_lr (self)
def	step (self, epoch=None)

Public Attributes
	step_size
	gamma
	last_epoch
Public Attributes inherited from torch.optim.lr_scheduler._LRScheduler
	optimizer
	base_lrs
	last_epoch

Detailed Description

Decays the learning rate of each parameter group by gamma every
step_size epochs. Notice that such decay can happen simultaneously with
other changes to the learning rate from outside this scheduler. When
last_epoch=-1, sets initial lr as lr.

Args:
    optimizer (Optimizer): Wrapped optimizer.
    step_size (int): Period of learning rate decay.
    gamma (float): Multiplicative factor of learning rate decay.
        Default: 0.1.
    last_epoch (int): The index of last epoch. Default: -1.

Example:
    >>> # Assuming optimizer uses lr = 0.05 for all groups
    >>> # lr = 0.05     if epoch < 30
    >>> # lr = 0.005    if 30 <= epoch < 60
    >>> # lr = 0.0005   if 60 <= epoch < 90
    >>> # ...
    >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
    >>> for epoch in range(100):
    >>>     scheduler.step()
    >>>     train(...)
    >>>     validate(...)

Definition at line 126 of file lr_scheduler.py.

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The documentation for this class was generated from the following file:

• torch/optim/lr_scheduler.py